Advanced Autonomy on a Low-Cost Educational Drone Platform

PiDrone is a quadrotor platform created to accompany an introductory robotics course. Students build an autonomous flying robot from scratch and learn to program it through assignments and projects. Existing educational robots do not have significant autonomous capabilities, such as high-level planning and mapping. We present a hardware and software framework for an autonomous aerial robot, in which all software for autonomy can run onboard the drone, implemented in Python. We present an Unscented Kalman Filter (UKF) for accurate state estimation. Next, we present an implementation of Monte Carlo (MC) Localization and FastSLAM for Simultaneous Localization and Mapping (SLAM). The performance of UKF, localization, and SLAM is tested and compared to ground truth, provided by a motion-capture system. Our evaluation demonstrates that our autonomous educational framework runs quickly and accurately on a Raspberry Pi in Python, making it ideal for use in educational settings.

Authors

• Luke Eller
• Théo Guérin
• Baichuan Huang
• Garrett Warren
• Sophie Yang
• Josh Roy
• Stefanie Tellex

Keywords

• Location awareness
• Simultaneous localization and mapping
• Monte Carlo methods
• Educational robots
• Software
• Planning
• State estimation
• Drones
• Python
• Quadrotors
• Educational Platform
• Drone Platform
• Autonomic System
• Kalman Filter
• Motion Capture
• Motion Capture System
• Introductory Course
• Raspberry Pi
• Unscented Kalman Filter
• Accurate State Estimation
• High-level Planner
• Error Of The Mean
• Inertial Measurement Unit
• Surface Texture
• Local Algorithm
• Optical Flow
• Motion Model
• Particle Position
• State Transition Function
• Extended Kalman Filter
• Robot Operating System
• Proportional-integral-derivative
• Feature Matching
• Modular Architecture
• Update Function
• Position Estimation
• Filtering Algorithm
• Brown University